Covid-19 Forecasting (NG)
Hello there, I've been gone for a long time, but you know, not like it was noticable or anything. cries in Eigenvectors. Well, if you must know the reason for my being gone for such a long time then look no further than: work, school, work, sleep, anime, sleep and just being generally lazy, like everyone I know this period except way before the headstart. I don't have that much strength to go in depth about it so I WON'T. Anyhow, 2020?
Let's get to today's task: Forecasting confirmed COVID-19 cases. I would have to put it up on a 'Beginner's pedastal' so it should be super easy to follow. Also, the case focus would be my home country: Nigeria.
If you'd like to view a COVID-19 Dashboard, I made one a couple of weeks back for just African countries: click here to view the dashboard
Let's get to it!
In [ ]:
In [1]:
#Import packages/tools
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sb #look at me not following norms.
import plotly.express as px #I like this one
from sklearn.preprocessing import StandardScaler
# from sklearn.base import BaseEstimator, TransformerMixin
# from sklearn.pipeline import Parallel, Pipeline, FeatureUnion
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import NuSVR
from sklearn.linear_model import LinearRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import BayesianRidge
from sklearn.model_selection import KFold
import warnings
warnings.filterwarnings('error','ignore')
In [240]:
sb.set(style='white', font_scale=1.2, rc={"figure.figsize": (15,10)})
colors_ = {
'background': '#111111',
'text': '#BEBEBE',
'grid': '#333333',
'red': '#BF0000'
}
tickFont = {
'size':12,
'color':"rgb(30,30,30)",
'family':"Courier New, monospace"}
SEED = 9
RANDOM_STATE = 5
In [241]:
from plotly.offline import init_notebook_mode,plot,iplot
import cufflinks
init_notebook_mode()
cufflinks.go_offline()
pd.set_option("max_rows", 500)
In [242]:
data = pd.read_csv("~/NG_covid19_ML/data/allData.csv")\
.drop(["Province/State", "CumDeaths", "CumRecovered"], axis=1)\
.astype({'date':'datetime64[ns]'})
data.head(3)
Out[242]:
| Country/Region | date | CumConfirmed | |
|---|---|---|---|
| 0 | Afghanistan | 2020-01-22 | 0 |
| 1 | Albania | 2020-01-22 | 0 |
| 2 | Algeria | 2020-01-22 | 0 |
In [243]:
#rename Country/Region to just Country
data.rename(columns={'Country/Region':'Country'}, inplace=True)
In [244]:
data.isna().sum() #No null values. Good!
Out[244]:
Country 0 date 0 CumConfirmed 0 dtype: int64
In [ ]:
Let's dedicate a couple of lines to making graphs: worldwide and then Africa.
In [245]:
countries_to_plot = ["US", "Italy", "China", "Germany", "Ghana", "Morocco", "Nigeria", "South Africa", "Tunisia"]
country_group = data.groupby(['date', 'Country'], as_index=False)['CumConfirmed'].sum()
xc = country_group[country_group["Country"].isin(countries_to_plot)]
trend_fig = px.line(xc, x="date", y="CumConfirmed", color="Country").for_each_trace\
(lambda rmvequalsign: rmvequalsign.update(name=rmvequalsign.name.split("=")[-1]))
trend_fig.update_traces(
line=dict(width=3),
selector=dict(type="scatter", mode="lines")
)
trend_fig.update_layout({'legend_orientation':'h'}, font=tickFont)\
.update_xaxes(title="", tickfont=tickFont)\
.update_yaxes(title="Cumulated Confirmed Cases")
#african countries have lower cases so it's hard sighting them on the graph\
#(thousands compared to a million)
In [246]:
countries_to_plot = ["Ghana", "Morocco", "Nigeria", "South Africa", "Tunisia", "Kenya"] #6 african countries
country_group = data.groupby(['date', 'Country'], as_index=False)['CumConfirmed'].sum()
xc = country_group[country_group["Country"].isin(countries_to_plot)]
trend_fig = px.line(xc, x="date", y="CumConfirmed", color="Country").for_each_trace\
(lambda rmvequalsign: rmvequalsign.update(name=rmvequalsign.name.split("=")[-1]))
trend_fig.update_traces(
line=dict(width=3),
selector=dict(type="scatter", mode="lines")
)
trend_fig.update_layout({'legend_orientation':'h'}, font=tickFont)\
.update_xaxes(title="", tickfont=tickFont)\
.update_yaxes(title="Cumulated Confirmed Cases")
iplot(trend_fig)
In [ ]:
In [247]:
#I shall be extracting data for Nigeria only
NG_data = data[data["Country"] == "Nigeria"]
NG_data.reset_index(drop=True, inplace=True)
In [248]:
#peep into the data and check last accounted date
NG_data.tail() #29th, April 2020
Out[248]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 94 | Nigeria | 2020-04-25 | 1182 |
| 95 | Nigeria | 2020-04-26 | 1273 |
| 96 | Nigeria | 2020-04-27 | 1337 |
| 97 | Nigeria | 2020-04-28 | 1532 |
| 98 | Nigeria | 2020-04-29 | 1728 |
In [ ]:
Going through the data I noticed that the 20th and 21st of April had the same value of 665, which would indicate that there was no case on the 21st but there was. One of the highest cases ever recorded: 117. I checked the NCDC's site to confirm and made the required changes to that index. Also, the first case in Nigeria wasn't till the 28th of February so I had to tailor the data starting from the 27th of February, just to get rid of that long list of zeros.
Let's investigate together!
In [249]:
NG_data[NG_data["CumConfirmed"] > 0].head(12) #easily identify first case - 28th.
#No further case until the 9th of March. Take note of the index (37, so tailor from 36)
Out[249]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 37 | Nigeria | 2020-02-28 | 1 |
| 38 | Nigeria | 2020-02-29 | 1 |
| 39 | Nigeria | 2020-03-01 | 1 |
| 40 | Nigeria | 2020-03-02 | 1 |
| 41 | Nigeria | 2020-03-03 | 1 |
| 42 | Nigeria | 2020-03-04 | 1 |
| 43 | Nigeria | 2020-03-05 | 1 |
| 44 | Nigeria | 2020-03-06 | 1 |
| 45 | Nigeria | 2020-03-07 | 1 |
| 46 | Nigeria | 2020-03-08 | 1 |
| 47 | Nigeria | 2020-03-09 | 2 |
| 48 | Nigeria | 2020-03-10 | 2 |
In [250]:
NG_data = NG_data.iloc[36:, :] #Tailored!
NG_data.reset_index(drop=True, inplace=True)
NG_data.head()
Out[250]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 0 | Nigeria | 2020-02-27 | 0 |
| 1 | Nigeria | 2020-02-28 | 1 |
| 2 | Nigeria | 2020-02-29 | 1 |
| 3 | Nigeria | 2020-03-01 | 1 |
| 4 | Nigeria | 2020-03-02 | 1 |
In [251]:
NG_data.tail(8) #note index 53 and 54
Out[251]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 55 | Nigeria | 2020-04-22 | 873 |
| 56 | Nigeria | 2020-04-23 | 981 |
| 57 | Nigeria | 2020-04-24 | 1095 |
| 58 | Nigeria | 2020-04-25 | 1182 |
| 59 | Nigeria | 2020-04-26 | 1273 |
| 60 | Nigeria | 2020-04-27 | 1337 |
| 61 | Nigeria | 2020-04-28 | 1532 |
| 62 | Nigeria | 2020-04-29 | 1728 |
In [252]:
NG_data.iplot(x='date', y='CumConfirmed')
Could you tell that flat curve just after 'Apr 19,' yeah? That's the culprit; 665 both on the 20th and 21st, while this could be a good thing meaning Nigeria had no new case on the 21st, we know that's false. In fact, we had 117 new cases on the 21st, summing it to 782. Hence, (20th) 665 - (21st) 782 - (22nd) 873 and so forth.
Also, NCDC's correction for the 18th of April: 541 instead of 542. Index 51.
Proof has been included below and update is to be made.
In [253]:
#index 54, column CumConfirmed, replace 665 with 782
NG_data.at[54, 'CumConfirmed'] = 782
NG_data.iloc[53:56, :] #perfection!
Out[253]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 53 | Nigeria | 2020-04-20 | 665 |
| 54 | Nigeria | 2020-04-21 | 782 |
| 55 | Nigeria | 2020-04-22 | 873 |
In [254]:
NG_data.at[51, 'CumConfirmed'] = 541
NG_data.iloc[50:52, :]
Out[254]:
| Country | date | CumConfirmed | |
|---|---|---|---|
| 50 | Nigeria | 2020-04-17 | 493 |
| 51 | Nigeria | 2020-04-18 | 541 |
In [255]:
#re-draw.
px.line(NG_data, x='date', y='CumConfirmed')
Now, let's use dataFrame.diff() on the CumConfirmed Column so we can get the daily cases from it, convert possible NaNs to zeros, which should be only one at the very top(0 - 'no value' = NaN)
In [256]:
NG_data["DailyConfirmed"] = NG_data["CumConfirmed"].diff()
NG_data.head()
Out[256]:
| Country | date | CumConfirmed | DailyConfirmed | |
|---|---|---|---|---|
| 0 | Nigeria | 2020-02-27 | 0 | NaN |
| 1 | Nigeria | 2020-02-28 | 1 | 1.0 |
| 2 | Nigeria | 2020-02-29 | 1 | 0.0 |
| 3 | Nigeria | 2020-03-01 | 1 | 0.0 |
| 4 | Nigeria | 2020-03-02 | 1 | 0.0 |
In [257]:
NG_data.fillna(axis=1, value=0, inplace=True)
NG_data.head(5)
Out[257]:
| Country | date | CumConfirmed | DailyConfirmed | |
|---|---|---|---|---|
| 0 | Nigeria | 2020-02-27 | 0 | 0.0 |
| 1 | Nigeria | 2020-02-28 | 1 | 1.0 |
| 2 | Nigeria | 2020-02-29 | 1 | 0.0 |
| 3 | Nigeria | 2020-03-01 | 1 | 0.0 |
| 4 | Nigeria | 2020-03-02 | 1 | 0.0 |
In [258]:
#cases on a daily basis
px.line(NG_data, x='date', y='DailyConfirmed')
Now, let's slip in data for our testing. I want to make predictions for the next 8 days, starting on the 28th, and then merge with our data on the row level.
In [1]:
post_date = ['2020-05-01', '2020-05-02', '2020-05-03', '2020-05-04', '2020-05-05', '2020-05-06', '2020-05-07', '2020-05-08']
post_incident = pd.DataFrame(post_date, columns=["date"])
post_incident["date"] = post_incident['date'].astype('datetime64[ns]')
post_incident.head(5)
--------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-469bf281df16> in <module> 1 post_date = ['2020-05-01', '2020-05-02', '2020-05-03', '2020-05-04', '2020-05-05', '2020-05-06', '2020-05-07', '2020-05-08'] ----> 2 post_incident = pd.DataFrame(post_date, columns=["date"]) 3 post_incident["date"] = post_incident['date'].astype('datetime64[ns]') 4 post_incident.head(5) NameError: name 'pd' is not defined
In [260]:
covid19_NG = pd.concat([NG_data, post_incident], sort=False, axis=0)
covid19_NG.reset_index(drop=True, inplace=True)
covid19_NG.tail(15)
Out[260]:
| Country | date | CumConfirmed | DailyConfirmed | |
|---|---|---|---|---|
| 56 | Nigeria | 2020-04-23 | 981.0 | 108.0 |
| 57 | Nigeria | 2020-04-24 | 1095.0 | 114.0 |
| 58 | Nigeria | 2020-04-25 | 1182.0 | 87.0 |
| 59 | Nigeria | 2020-04-26 | 1273.0 | 91.0 |
| 60 | Nigeria | 2020-04-27 | 1337.0 | 64.0 |
| 61 | Nigeria | 2020-04-28 | 1532.0 | 195.0 |
| 62 | Nigeria | 2020-04-29 | 1728.0 | 196.0 |
| 63 | NaN | 2020-04-30 | NaN | NaN |
| 64 | NaN | 2020-05-01 | NaN | NaN |
| 65 | NaN | 2020-05-02 | NaN | NaN |
| 66 | NaN | 2020-05-03 | NaN | NaN |
| 67 | NaN | 2020-05-04 | NaN | NaN |
| 68 | NaN | 2020-05-05 | NaN | NaN |
| 69 | NaN | 2020-05-06 | NaN | NaN |
| 70 | NaN | 2020-05-07 | NaN | NaN |
In [261]:
covid19_NG["day"] = covid19_NG.date.dt.strftime("%m%d").astype('int')
covid19_NG.head()
Out[261]:
| Country | date | CumConfirmed | DailyConfirmed | day | |
|---|---|---|---|---|---|
| 0 | Nigeria | 2020-02-27 | 0.0 | 0.0 | 227 |
| 1 | Nigeria | 2020-02-28 | 1.0 | 1.0 | 228 |
| 2 | Nigeria | 2020-02-29 | 1.0 | 0.0 | 229 |
| 3 | Nigeria | 2020-03-01 | 1.0 | 0.0 | 301 |
| 4 | Nigeria | 2020-03-02 | 1.0 | 0.0 | 302 |
I checked around for news that could be of value in terms of feature engineering.
21-03-2020: NG bans entry of travelers from 13 countries(incl. US, Uk, Italy, France and China) [ng.usembassy.gov] Ban to last for 4 weeks.¶
28-03-2020: GTB hands over 110-bed isolation centre to Lagos State Gov.
08-04-2020: 110-bed isolation centre by GTB begins operation. [Allafrica]('https://allafrica.com']¶
31-03-2020: Lockdown as announced by Pres. Buhari (Starts 11:30PM on the 30th, so next day then)¶
14-04-2020: Another lockdown starts. 04-03-2020: No single isolation centre in Abuja says Senate President. [Punch.ng] 27-01-2020: First Nigeria case. NCDC
21-02-2020: covid19 training starts ncdc.gov.ng¶
I contemplated using the bolded ones and ended up picking just one: Travel Ban. Lockdown started looking like Travel Ban after some days so I just dropped it.
In [262]:
def lk(val):
if val >= 331 and val <= 404:
return "Relaxed"
elif val >= 405 and val <=414:
return "Tight"
elif val <= 330:
return "Not enforced"
else:
return "Relaxed"
In [263]:
covid19_NG["Lockdown"] = covid19_NG["day"].apply(lk)
covid19_NG[covid19_NG["Lockdown"] == "Relaxed"].head(3)
Out[263]:
| Country | date | CumConfirmed | DailyConfirmed | day | Lockdown | |
|---|---|---|---|---|---|---|
| 33 | Nigeria | 2020-03-31 | 135.0 | 4.0 | 331 | Relaxed |
| 34 | Nigeria | 2020-04-01 | 174.0 | 39.0 | 401 | Relaxed |
| 35 | Nigeria | 2020-04-02 | 184.0 | 10.0 | 402 | Relaxed |
In [264]:
covid19_NG["Travel Ban"] = covid19_NG["day"].apply(lambda x: "Enforced" if x >= 321 else "Not Enforced")
covid19_NG[covid19_NG["Travel Ban"] == "Enforced"].head(3)
#321 - 21st of March, when ban became effective.
Out[264]:
| Country | date | CumConfirmed | DailyConfirmed | day | Lockdown | Travel Ban | |
|---|---|---|---|---|---|---|---|
| 23 | Nigeria | 2020-03-21 | 22.0 | 10.0 | 321 | Not enforced | Enforced |
| 24 | Nigeria | 2020-03-22 | 30.0 | 8.0 | 322 | Not enforced | Enforced |
| 25 | Nigeria | 2020-03-23 | 40.0 | 10.0 | 323 | Not enforced | Enforced |
In [265]:
covid19_NG["day_name"] = covid19_NG.date.dt.day_name()
covid19_NG.head()
Out[265]:
| Country | date | CumConfirmed | DailyConfirmed | day | Lockdown | Travel Ban | day_name | |
|---|---|---|---|---|---|---|---|---|
| 0 | Nigeria | 2020-02-27 | 0.0 | 0.0 | 227 | Not enforced | Not Enforced | Thursday |
| 1 | Nigeria | 2020-02-28 | 1.0 | 1.0 | 228 | Not enforced | Not Enforced | Friday |
| 2 | Nigeria | 2020-02-29 | 1.0 | 0.0 | 229 | Not enforced | Not Enforced | Saturday |
| 3 | Nigeria | 2020-03-01 | 1.0 | 0.0 | 301 | Not enforced | Not Enforced | Sunday |
| 4 | Nigeria | 2020-03-02 | 1.0 | 0.0 | 302 | Not enforced | Not Enforced | Monday |
In [266]:
sb.barplot(data=covid19_NG, x="day_name", y="DailyConfirmed")
Out[266]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f91d14bbdd8>
In [267]:
print("Highest reported cases in Nigeria: ", covid19_NG["DailyConfirmed"].max())
covid19_NG[covid19_NG["DailyConfirmed"] == covid19_NG["DailyConfirmed"].max()]
Highest reported cases in Nigeria: 196.0
Out[267]:
| Country | date | CumConfirmed | DailyConfirmed | day | Lockdown | Travel Ban | day_name | |
|---|---|---|---|---|---|---|---|---|
| 62 | Nigeria | 2020-04-29 | 1728.0 | 196.0 | 429 | Relaxed | Enforced | Wednesday |
In [ ]:
In [268]:
covid19_NG.drop(["Country", "date"], axis=1, inplace=True) #drop country and date
In [269]:
#Log transform DailyConfirmed. NaNs won't be transformed
covid19_NG["DailyConfirmed"] = np.log1p(covid19_NG["DailyConfirmed"])
In [270]:
covid19_NG = pd.get_dummies(covid19_NG, drop_first=True) #get dummies
covid19_NG.head()
Out[270]:
| CumConfirmed | DailyConfirmed | day | Lockdown_Relaxed | Lockdown_Tight | Travel Ban_Not Enforced | day_name_Monday | day_name_Saturday | day_name_Sunday | day_name_Thursday | day_name_Tuesday | day_name_Wednesday | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.000000 | 227 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 1.0 | 0.693147 | 228 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1.0 | 0.000000 | 229 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 1.0 | 0.000000 | 301 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | 1.0 | 0.000000 | 302 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
Let's split the data into train, validation and testing.
In [271]:
train = covid19_NG[covid19_NG["day"] <= 427] #Train on this
val = covid19_NG[(covid19_NG["day"] >= 428) & (covid19_NG["day"] <= 429)] #validate
test = covid19_NG[covid19_NG["day"] >= 430] #test
In [272]:
print("Original data shape: ", covid19_NG.shape, "\nTrain shape: ", train.shape, "\nValidation data shape", val.shape, "\nTest data shape: ",test.shape)
Original data shape: (71, 12) Train shape: (61, 12) Validation data shape (2, 12) Test data shape: (8, 12)
In [ ]:
Get ready for TRAINING and TESTING!
In [273]:
Xtrain = train.drop(["CumConfirmed", "DailyConfirmed"], axis=1)
ytrain = train["DailyConfirmed"]
Xval = val.drop(["CumConfirmed", "DailyConfirmed"], axis=1)
yval = val["DailyConfirmed"]
Xtest = test.drop(["CumConfirmed", "DailyConfirmed"], axis=1)
Xtrain.head(3)
Out[273]:
| day | Lockdown_Relaxed | Lockdown_Tight | Travel Ban_Not Enforced | day_name_Monday | day_name_Saturday | day_name_Sunday | day_name_Thursday | day_name_Tuesday | day_name_Wednesday | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 227 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 228 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 229 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
In [ ]:
In [274]:
ss = StandardScaler()
In [275]:
Xtrain = ss.fit_transform(Xtrain)
Xval = ss.transform(Xval)
Xtest = ss.transform(Xtest)
In [ ]:
In [276]:
from sklearn.metrics import mean_squared_error as mse
In [277]:
br = BayesianRidge(n_iter=1000)
gu = GaussianNB()
In [2]:
cat_params = {
'num_boost_round':50000,
'learning_rate': 0.001,
'silent':True,
'eval_metric': 'RMSE',
'early_stopping_rounds':100,
'max_depth':7,
'use_best_model':True,
'random_state': RANDOM_STATE
}
--------------------------------------------- NameError Traceback (most recent call last) <ipython-input-2-a5430a2f685c> in <module> 7 'max_depth':7, 8 'use_best_model':True, ----> 9 'random_state': RANDOM_STATE 10 } NameError: name 'RANDOM_STATE' is not defined
In [293]:
cat_model = CatBoostRegressor(**cat_params)
rf_model = RandomForestRegressor(n_estimators=80000, random_state=RANDOM_STATE)
nuSVR = NuSVR(C=100, cache_size=400, tol=0.005, gamma='auto', coef0=0.05)
#¶
In [294]:
cat_model.fit(Xtrain, ytrain, eval_set=[(Xval, yval)])
print("Best Score: ", cat_model.best_score_, "\nIteration stopped at: ", cat_model.best_iteration_)
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Stopped by overfitting detector (100 iterations wait)
bestTest = 1.009661446
bestIteration = 17454
Shrink model to first 17455 iterations.
Best Score: {'learn': {'RMSE': 0.3690850773677326}, 'validation': {'RMSE': 1.0096614456292605}}
Iteration stopped at: 17454
In [296]:
train_pred_cat = cat_model.predict(Xtrain)
print("RMSE: ", np.sqrt(mse(train_pred_cat, np.expm1(ytrain))))
RMSE: 36.22464643034962
In [297]:
val_pred = cat_model.predict(Xval)
print("Predicted values: ", list(np.round(np.expm1(val_pred), decimals=0)))
print("Actual values: ", list(np.round(np.expm1(yval), decimals=0)))
print("RMSE: ", np.sqrt(mse(val_pred, np.expm1(yval))))
Predicted values: [67.0, 75.0] Actual values: [195.0, 196.0] RMSE: 191.22793506573979
In [298]:
test_pred_cat = cat_model.predict(Xtest)
test_pred_cat #log-transformed
Out[298]:
array([4.22158053, 4.58461073, 4.25557375, 4.37896889, 4.28387889,
4.21364079, 4.3315064 , 4.22158053])
In [285]:
# xg_model = XGBRegressor(n_estimators=20000, learning_rate=0.0005)
### Random Forest¶
In [286]:
rf_model.fit(Xtrain, ytrain)
Out[286]:
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=80000,
n_jobs=None, oob_score=False, random_state=5, verbose=0,
warm_start=False)
In [287]:
train_pred_rf = rf_model.predict(Xtrain)
print("RMSE (TRAIN): ", np.sqrt(mse(train_pred_rf, np.expm1(ytrain))))
print("######")
##evaluation
val_pred_rf = rf_model.predict(Xval)
print("Predicted values: ", list(np.round(np.expm1(val_pred_rf), decimals=0)))
print("Actual values: ", list(np.round(np.expm1(yval), decimals=0)))
print("RMSE (EVALUATION): ", np.sqrt(mse(val_pred_rf, np.expm1(yval))))
##test prediction
test_pred_rf = rf_model.predict(Xtest)
RMSE (TRAIN): 36.17916344976463 ###### Predicted values: [89.0, 84.0] Actual values: [195.0, 196.0] RMSE (EVALUATION): 191.02533512859262
In [288]:
# list(np.round(np.expm1(covid19_NG.DailyConfirmed[:-9]), decimals=0))
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In [289]:
cat_pred = np.round(np.expm1(test_pred_cat), decimals=0)
rf_pred = np.round(np.expm1(test_pred_rf), decimals=0)
avr = (cat_pred + rf_pred)/2
In [290]:
test_predictions = pd.DataFrame({
"date": post_incident["date"],
"New Cases (CAT)": [int(x) for x in cat_pred],
"New Cases (Random Forest)": [int(x) for x in rf_pred],
"New Cases (on average)": [int(x) for x in (np.round(avr, decimals=0))]
})
test_predictions
Out[290]:
| date | New Cases (CAT) | New Cases (Random Forest) | New Cases (on average) | |
|---|---|---|---|---|
| 0 | 2020-04-30 | 68 | 87 | 78 |
| 1 | 2020-05-01 | 96 | 86 | 91 |
| 2 | 2020-05-02 | 70 | 84 | 77 |
| 3 | 2020-05-03 | 79 | 84 | 82 |
| 4 | 2020-05-04 | 72 | 72 | 72 |
| 5 | 2020-05-05 | 65 | 89 | 77 |
| 6 | 2020-05-06 | 75 | 84 | 80 |
| 7 | 2020-05-07 | 68 | 87 | 78 |
Overfitting was expected and owing largely to the small data size.
In [291]:
test_predictions.to_csv("~/NG_covid19_ML/data/submission.csv")
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# from IPython.display import Image
# Image("gifs/hinata.gif", width='80%')
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